Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning
This is an incremental improvement for autonomous driving systems.
The paper tackles autonomous lane change by applying Deep Q-network with safety considerations for decision-making and designing frameworks for gap selection and following, achieving effectiveness in simulation.
We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver. Furthermore, we design two similar Deep Q learning frameworks with quadratic approximator for deciding how to select a comfortable gap and just follow the preceding vehicle. Finally, a polynomial lane change trajectory is generated and Pure Pursuit Control is implemented for path tracking. We demonstrate the effectiveness of this framework in simulation, from both the decision-making and control layers. The proposed architecture also has the potential to be extended to other autonomous driving scenarios.